Behind every AI prompt: The infrastructure powering billions of daily queries

Every time a seemingly simple question, like “Is a tomato a fruit?” is posed to a generative AI model, vast computational infrastructure is activated. GPUs whirr into action, computing quickly to deliver a response. In this case, the answer is: ‘Yes, it is,’ but there is a whole world of complexity behind the scenes that enables the delivery of this simple answer, at a quick speed.

Let’s look at numbers for perspective. There are over six billion internet users in the world¹ and an estimated 1.5 to 2 billion people are interacting with AI globally. The volume of daily queries is enormous. China is the only country that tracks and publishes AI token data, and the country’s daily token usage has already surpassed the 140 trillion mark3. A token is text broken into smaller units that AI models use to understand and generate language.

At this scale, even minor inefficiencies multiply into massive costs and power overheads. This is where AI inference architecture may become critical.

The importance of inference

A Large Language model (LLM) is trained in the cloud on vast datasets and refined to produce reliable language predictions. Once deployed, the model responds to entirely new questions or inputs it has never encountered before. That process is known as inference.

During inference, the model processes the input tokens and generates a response in seconds. There are two stages of inference, each dependent on compute and memory, respectively. During the first stage, known as prefill, the model ingests the prompt and builds contextual understanding. For example, in the phrase “Meet me at the riverbank,” it correctly interprets “bank” as the edge of a river rather than a financial institution. This stage is constrained primarily by GPU compute.

The second stage, decode, generates the response of one token at a time and is primarily constrained by memory capacity and bandwidth.

At scale, serving millions of users, these two stages expose fundamental infrastructure constraints. Firstly, prefill requires massive parallel GPU resources operating continuously at high utilization. GPUs are scarce, expensive, and power-intensive. Secondly, decode depends on high-capacity, high-bandwidth memory, which is both costly and limited. Together, these constraints create a central challenge: delivering fast, cost-efficient AI responses without scaling compute and memory infrastructure.

This is where KV cache (Key-Value Cache) comes in. KV cache stores intermediate computations during token generation, allowing the model to reuse prior context instead of recomputing it. Without it, generating each new token would require reprocessing the entire input sequence, dramatically reducing efficiency.

To make this concrete, let’s return to our opening question, ‘Is a tomato a fruit?” An LLM generates its response incrementally, one token at a time. It computes ‘Yes,’ then computes again and produces ‘it,’ forming ‘Yes, it’, and finally ‘is’, generating the complete answer: Yes, it is.’ Each step is stored in the KV cache in a mathematical form.  To understand why this matters, it helps to think in everyday terms.

Imagine a researcher in a library writing a summary report based on a reference book. Without KV cache, this is analogous to the researcher repeatedly rereading an entire book to write each new sentence, an inefficient process that can be eliminated by retaining working memory. By storing intermediate results during the prefill stage in the KV cache, the model no longer needs to recompute prior tokens, freeing up the GPU to focus on generating new ones.

Persistent KV Cache

By reducing the load on GPUs, KV cache helps with more optimal utilization, consequently helping alleviate the ever-growing power demand predicament. KV cache in GPU memory is ephemeral; it is discarded after inactivity to free expensive resources.

Persistent KV cache extends this concept by storing KV cache in non-volatile Flash storage, can help support higher GPU utilization, lower cost per query and context continuity across sessions. This creates a strong case for flash storage as a key component in AI infrastructure.

KV cache continues to scale with the growth of a model or increase in traffic or questions. Flash storage or NVMe™ SSDs (solid state drives) have emerged as a commonly used option for storing persistent KV cache.

The relentless innovation in flash storage has led to the development of high-capacity (up to 256TB) and high-performance drives that have become a widely adopted approach for storing persistent KV cache; In doing so, flash storage is helping with deeper proliferation of AI, while helping to improve power efficiency.

As our expectation of AI continues to swell, the demand for better and faster responses in expected format and language are only increasing. As AI adoption accelerates, infrastructure efficiency will define scalability. What begins as a simple question like “Is a tomato a fruit?”, ultimately depends on innovations in compute, memory, and storage. KV cache, and especially its evolution toward persistent implementations, represents a meaningful step toward helping make AI faster, more efficient, and economically viable.

The author is Senior Director of Marketing, Sandisk. 

Disclaimer: The views expressed are the author’s own and do not reflect the official policy or position of Financial Express.